2026-03-14T02:27:00Z https://kanazawa-u.repo.nii.ac.jp/oai

oai:kanazawa-u.repo.nii.ac.jp:00007542 2024-06-20T06:16:24Z 934:935:936

A cascade form blind source separation connecting source separation and linearization for nonlinear mixtures 00207945 Nakayama, Kenji Hirano, Akihiro Nishiwaki, Takayuki A network structure and its learning algorithm have been proposed for blind source separation applied to nonlinear mixtures. The network has a cascade form consists of a source separation block and a linearization block in this order. The conventional learning algorithm is employed for the separation block. A new learning algorithm is proposed for the linearization block assuming 2nd-order nonlinearity. After, source separation, the outputs include the nonlinear components for the same signal source. This nonlinearity is suppressed through the linearization block. Parameters in this block are iteratively adjusted based on a process of solving a 2nd-order equation of a single variable. Simulation results, using 2-channel speech signals and an instantaneous nonlinear mixing process, show good separation performance. IEEE(Institute of Electrical and Electronics Engineers) 2003-07-01 eng conference paper VoR http://hdl.handle.net/2297/6855 https://kanazawa-u.repo.nii.ac.jp/records/7542 Proceedings of the International Joint Conference on Neural Networks 3 1856 1861 https://kanazawa-u.repo.nii.ac.jp/record/7542/files/TE-PR-NAKAYAMA-K-20030702.pdf application/pdf 202.5 kB 2017-10-03